Machine Learning Basics: A Speedrun - IPAM at UCLA

Embark on a fast-paced journey through machine learning fundamentals in this 1-hour 8-minute tutorial presented by Stefan Chmiela from Technische Universität Berlin at IPAM's Advancing Quantum Mechanics with Mathematics and Statistics Tutorials. Dive into key concepts such as inductive bias, underfitting and overfitting, optimal model complexity, and regularization techniques. Explore linear and nonlinear regression, kernel methods, and matrix factorization. Gain insights into data limitations, cross-validation, and the kernel trick. Discover how these principles apply to energy contributions and iterative optimization techniques, concluding with a discussion on the tradeoffs involved in nonlinear approaches.

Intro
Parameters
Inductive bias
Underfitting and overfitting
Considerations
Illustration
Optimal model complexity
Regularization terms
Crossvalidation
Data limitations
Linear regression
Ridge regression
Nonlinear regression
Kernel track
Kernel retrogression
Kernel as linear operator
Kernel trick
Energy contributions
Matrix factorization
Matrix iterative optimization
Preconditioning
Tradeoff
Nonlinearity